Automated Tuning of End-to-end Neural Flight Controllers for Autonomous Nano-drones
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Date
2021-06-06
Publication Type
Conference Paper
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yes
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Abstract
Convolutional neural networks (CNNs) are fueling the advancement of autonomous palm-sized drones, i.e., nano-drones, despite their limited power envelope and onboard processing capabilities. Computationally lighter than traditional geometrical approaches, CNNs are the ideal candidates to predict end-to-end signals directly from the sensor inputs to feed to the onboard flight controller. However, these sophisticated CNNs require significant complexity reduction and fine-grained tuning to be successfully deployed aboard a flying nano-drone. To date, these optimizations are mostly hand-crafted and require error-prone, labor-intensive iterative development flows. This work discusses methodologies and software tools to streamline and automate all the deployment stages on a low-power commercial multicore System-on-Chip. We investigate both an industrial closed-source and an academic open-source tool-set with a field-proofed state-of-the-art CNN for autonomous driving. Our results show a 2 reduction of the memory footprint and a speedup of 1.6 in the inference time, compared to the original hand-crafted CNN, with the same prediction accuracy.
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Publication status
published
Editor
Book title
2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Journal / series
Volume
Pages / Article No.
1 - 4
Publisher
IEEE
Event
3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS 2021)
Edition / version
Methods
Software
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Date created
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Organisational unit
03996 - Benini, Luca / Benini, Luca
Notes
Conference lecture held on June 8, 2021